CN107093002A - A kind of bore closed quality classification and hazard assessment system based on cloud computing - Google Patents
A kind of bore closed quality classification and hazard assessment system based on cloud computing Download PDFInfo
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Abstract
The invention discloses a kind of bore closed quality classification based on cloud computing and hazard assessment system, including inquiry terminal and cloud computing server, inquiry terminal is made up of microprocessor, display module, alarm module, input module and power supply module；The input that the output end of cloud computing server formulates module, sort module, hazard assessment module and enquiry module with external memory, emergency plan respectively is electrically connected with；Cloud computing server is electrically connected with RAM memory, mram memory and database respectively；The output end of cloud computing server is connected by GPRS network with the input of high in the clouds memory；The output end of sort module is electrically connected with the input of the first flash card and the second flash card respectively, the beneficial effect of the invention is bore closed quality analysis, classification and hazard assessment efficiency high, related data storage safety, the reason for bore closed is off quality can be exported rapidly and corresponding emergency measure is formulated.
Description
Technical field
The invention belongs to coal production field, more particularly to a kind of bore closed quality classification and danger based on cloud computing
Property evaluation system.
Background technology
Close bad drilling is to cause an important factor in order of prominent (gushing) the water accident in colliery.Applied in the different periods of history
The drilling of work, because a variety of causes causes some drillings to there are problems that seal quality, major hidden danger is brought to coal mining.Envelope
Surface water, aquifer water can be turned on, old empty water, can also turn on coal seam or old aerial gas or harmful by closing bad drilling
Gas, can also cause spontaneous combustion of coal seam.Waste gas drilling is disguised strong, and vertical depth is big, is difficult to take precautions against in production, and due to drilling
Coverage is small in plane, tends to ignored, therefore causes major accident.
But the local datastore of existing bore closed quality classification and hazard assessment system is not safe enough, causes
The loss of data, due to using simple storage mode can produce it is a large amount of repeat or invalid data, this storage to data,
Excavate and analysis causes added burden；On the other hand existing bore closed quality classification and hazard assessment system do not possess brill
Unqualified the used emergency measure function in hole so that the practical performance of system is substantially reduced.
The content of the invention
It is an object of the invention to provide a kind of bore closed quality classification based on cloud computing and hazard assessment system,
Aim to solve the problem that be existing bore closed quality classification and hazard assessment system local datastore it is not safe enough, cause number
According to loss, due to using simple storage mode can produce it is a large amount of repeat or invalid data, this storage to data, dig
Pick and analysis cause added burden；On the other hand existing bore closed quality classification and hazard assessment system do not possess drilling
Unqualified used emergency measure function so that the problem of practical performance of system is substantially reduced.
The present invention is achieved in that a kind of bore closed quality classification based on cloud computing and hazard assessment system,
Including inquiry terminal and cloud computing server, the inquiry terminal is by microprocessor, display module, alarm module, input module
Constituted with power supply module；
The output end of the microprocessor is electrically connected with the input of display module and alarm module respectively；Micro- place
Output end of the input of device respectively with input module and power supply module is managed to be electrically connected with；
The microprocessor is connected by Ethernet with data switching exchane；
The cloud computing server is connected by Ethernet with data switching exchane；
The input of the cloud computing server and the output end of data update module are electrically connected with；
The output end of the cloud computing server formulates module, sort module, danger with external memory, emergency plan respectively
Property evaluation module and enquiry module input be electrically connected with；
The input electricity that the output end of the cloud computing server passes through analysis of data collected module and data processing module
Property connection；
The cloud computing server is electrically connected with RAM memory, mram memory and database respectively；
The output end of the cloud computing server is connected by GPRS network with the input of high in the clouds memory；
The output end of the sort module is electrically connected with the input of the first flash card and the second flash card respectively；
The shell of the inquiry terminal is made of explosion-proof lamp component, and the display module is specially that anti-explosion LED is shown
Screen, the alarm module is specially LED flashing lamps, and the output end of the external memory and the input of data update module are electrical
Connection.Bore closed quality classification and hazard assessment is realized by the following method in the system；
The envelope of the drilling based on cloud computing of the bore closed quality classification and hazard assessment system based on cloud computing
Closing quality classification and method for evaluating hazard includes：
Step 1: according to drilling basic information database, bore closed materials volume and quality database and drilling coal
Layer information database three part, determine in drilling target reservoir in horizontal segment cytoplasmic mutation estimation range；
Step 2: according to all visual angles and the angle of pitch in the length of side of rectangle image device and current region to be detected, adjustment
Full shot on focal plane institute into circular panorama picture so that the full shot on focal plane into the straight of circular panorama picture
Footpath is more than the bond length of rectangle image device；
Step 3: ground model and well drilling rail model of the generation with constraints, set up general target plane equation
And the coordinate transformation relation between target coordinate system and mouth coordinate system, with the target plane suitable for various well type；
Step 4: drilling essential information data are uploaded into cloud computing center in real time；
Step 5: the population scale of initialization drilling essential information data, and population maximum iteration is set；
Step 6: chromosome is generated at random according to coding rule, by the abstract coding input for chromosome of cloud task, dyeing
Each gene in body is the subtask in the cloud task, and the position sequence number of chromosome represents that each subtask is represented and is assigned to virtually
The number of machine；Then judge whether chromosome meets expected Qos, if meeting, the chromosome be added in initial population,
If it is not satisfied, the chromosome is then abandoned, the scale until reaching initial population；
Step 7: judging the user's storage service request received, mistake is returned if service request is not met
False information；It is then storage service request distribution resource by global resource scheduler to meet service request；
Step 8: the deviational survey data based on last two measuring point of drilling trajectory, calculate the track characteristic parameter that section is surveyed at end, it is described
Deviational survey data are well depth, hole angle, azimuth, and the track characteristic parameter is used to characterize the last trajectory shape for surveying section；
Step 9: panorama original image is carried out into chromatic compensation to be compensated rear image, image after this is compensated with
Track characteristic parameter is spliced, and for one group of image of processing defined in multiple stitching images, is made in this group of image at least
One composition alignment, is converted by alignment by the way that one or more image is cut out, is sized and rotated
A series of one or more images to produce process conversion in image；
Step 10: bore closed quality classification evaluation method is from borehole image, the consumption of drilling and sealing material, bore closed
Section carries out classification quantitative assessment to the seal quality of drilling respectively away from three aspects, and at least two parties face evaluation result is qualified, judges
It is qualified for the seal quality of the drilling, otherwise, then determine that it is close bad drilling；
Step 11: close bad drills, water guide hazard assessment is by each dangerous influence factor of initiation drilling water guide
Intrusion Index is calculated and BP artificial neural network modules predict that two parts are constituted；
The basic information database that drills is again comprising the essential information that drills, and bore closed information, the 4th are and marl is aqueous
Layer information；Wherein essential information include drilling sequence number, title, latitude away from, through away from, aperture absolute altitude, drilling depth, whole aperture layer position；Bore
Hole closing information includes closing hole structure, bore closed start-stop depth, sealing material and consumption, sealing of hole overview and sampling matter
Amount；4th be and marl water-bearing layer information include Quaternary aquifer floor level, marl floor level and thickness；
Bore closed materials volume and quality database contain hole structure information and closed section away from information, hole structure
Information includes aperture, initial depth and terminates depth, closed section away from information include bore closed section away from initial depth, terminate it is deep
Degree, cement consumption and sand consumption；
Drilling coal seam information database contains the floor level that each coal seam cut is worn in drilling, coal seam thickness, Yi Jifeng
Hole section away from starting and terminate height, aperture absolute altitude and peak level；
Water guide danger influence factor includes water filling source, geological structure, mining work activities, and water filling source is again comprising the 4th
Water-bearing layer, the three sub- factors in mud stone water-bearing layer and old empty water, geological structure include tomography, fold, and mining work activities include water guide
Fissure zone development height and plate destructing depth, pass through the dangerous Intrusion Index of water guide of each influence factor of system-computed；
BP neural network predictions module includes parameters setting, sample training and sample predictions.
Panorama original image is spliced by original closure borehole image, the data square of image of the generation by splicing
Battle array, and centralization or standardization are carried out to data matrix, calculate the variance square of the data matrix after centralization or standardization
Battle array, high order proper polynomial is converted to by the proper polynomial of variance matrix, and standard is carried out to the data matrix according to following formula
Change：
In formula：
Obtain：
A=(Aij)m×n；
M is the line number of data matrix, and n is the columns of data matrix, i=1,2 ..., m, j=1,2 ..., n；Xij is data
The data that the i-th row jth is arranged in matrix.
Further, the microprocessor is provided with distributed power control module, the distributed power control module base
Above-mentioned Hamilton Jacobi Bellman equation and Fu Ke-Planck-Ke Ermoge love equations are solved in finite-difference algorithm,
So as to show that interference perceives power control strategy；
The Hamilton Jacobi Bellman equation is：
Wherein
The Fu Ke-Planck-Ke Ermoge love equations are：
Further, the distributed power control method of the distributed power control module comprises the following steps：
Step one, initialize, and time interval, the state space of dominant interferer and the state space typically disturbed are carried out
Discretization；The time interval, the state space of dominant interferer and the state space typically disturbed will be discretized as X × Y × Z
Space, the time, energy, the iteration step length of interference space is：
Step 2, judges whether to meet iterated conditional, if carrying out next step, otherwise stops iteration；The iterated conditional
Refer to that t, o, i meet t=1 simultaneously:S, o=1:Y and i=1:Z, carries out next step, otherwise stops iteration when eligible；Its
Middle s, Y and Z value are provided in initialization；
Step 3, upgrading interference mean field, and judge whether power level is zero, if it is further upgrading interference is flat
Equal field, if otherwise interference mean field is zero；The upgrading interference mean fieldWhen use formula：
WhereinIt is the mean field value at any point (t, o, i) place in discrete grid block,WithBe respectively dominant interferer and
The variance typically disturbed, soAnd siIt is the i of dominant interferer o and general user state kinetics, δ respectivelyoAnd δiIt is iteration step
It is long, wherein
Step 4, updates Lagrangian and power level, and according to iterated conditional, repeat step two arrives step 4；More
New LagrangianWhen use formula：
WhereinWithIt is the mean field value and cost function value at any point (t, o, i) place in discrete grid block respectively,WithIt is dominant interferer and the variance typically disturbed, s respectivelyoAnd siIt is the i of dominant interferer o and general user state power respectively
Learn, δo、δiAnd δtIt is iteration step length, wherein
Dominant interferer o power levelRenewal use following manner：
WhereinWithIt is the mean field value and cost function value at any point (t, o, i) place in discrete grid block, s respectivelyoWith
siIt is the i of dominant interferer o and general user state kinetics respectively,It is the transmission power of dominant interferer, δoAnd δiIt is iteration
Step-length, wherein
General user i power levelRenewal use following manner：
Wherein each alphabetical implication is referred to as above,It is the transmission power of general user's dominant interferer.
Bore closed quality classification based on cloud computing and hazard assessment system that the present invention is provided, pass through gathered data
Analysis module and data processing module are analyzed and processed to input data, and bore closed quality analysis, classification and danger are commented
Valency efficiency high；RAM memory, mram memory and database are used cooperatively, and the information of data can be contrasted, sampling and
Inquiry；The use of first flash card, the second flash card and high in the clouds memory so that related data storage safety, is not in mixed
Disorderly with omitting the problem of；Emergency plan, which formulates module, can judge rapidly to cause the reason for bore closed is off quality and system
Fixed corresponding emergency measure.Bore closed quality classification evaluation method is sealed from borehole image, the consumption of drilling and sealing material, drilling
Close section and classification quantitative assessment is carried out to the seal quality of drilling respectively away from three aspects, at least two parties face evaluation result is qualified, sentences
The seal quality for being set to the drilling is qualified, otherwise, then determines that it is close bad drilling；Close bad drilling water guide danger is commented
Valency is calculated by the Intrusion Index of each dangerous influence factor of initiation drilling water guide and BP artificial neural networks module predicts two
It is grouped into.Each drilling of comprehensive assessment draws bore closed quality classification evaluation result to the influence degree of seam mining, it is ensured that
Safety in production, the investigation and improvement that drilling can be closed for colliery provide referential experience.
Brief description of the drawings
Fig. 1 is the bore closed quality classification provided in an embodiment of the present invention based on cloud computing and hazard assessment system knot
Structure schematic diagram.
In figure：1st, terminal is inquired about；2nd, microprocessor；3rd, display module；4th, alarm module；5th, input module；6th, power supply mould
Block；7th, Ethernet；8th, data switching exchane；9th, cloud computing server；10th, data update module；11st, external memory；12nd, quick-acting prescription is answered
Case formulates module；13rd, sort module；14th, hazard assessment module；15th, enquiry module；16th, analysis of data collected module；17、
Data processing module；18th, RAM memory；19th, mram memory；20th, database；21st, GPRS network；22nd, high in the clouds memory；
23rd, the first flash card；24th, the second flash card.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
The application principle of the present invention is explained in detail below in conjunction with the accompanying drawings.
The structure of the present invention is explained in detail with reference to Fig. 1.
Bore closed quality classification and hazard assessment system provided in an embodiment of the present invention based on cloud computing, including look into
Terminal 1 and cloud computing server 9 are ask, the inquiry terminal 1 is by microprocessor 2, display module 3, alarm module 4, input module 5
Constituted with power supply module 6；The output end of the microprocessor 2 electrically connects with the input of display module 3 and alarm module 4 respectively
Connect；The input of the microprocessor 2 is electrically connected with the output end of input module 5 and power supply module 6 respectively；The microprocessor
Device 2 is connected by Ethernet 7 with data switching exchane 8；The cloud computing server 9 is connected by Ethernet 7 with data switching exchane 8
Connect；The input of the cloud computing server 9 is electrically connected with the output end of data update module 10；The cloud computing server
9 output end is formulated module 12, sort module 13, hazard assessment module 14 with external memory 11, emergency plan respectively and looked into
The input for asking module 15 is electrically connected with；The output end of the cloud computing server 9 passes through analysis of data collected module 16 and number
It is electrically connected with according to the input of processing module 17；The cloud computing server 9 respectively with RAM memory 18, mram memory 19
It is electrically connected with database 20；The output end of the cloud computing server 9 is defeated by GPRS network 21 and high in the clouds memory 22
Enter end connection；Input of the output end of the sort module 13 respectively with the first flash card 23 and the second flash card 24 electrically connects
Connect.
Further, the shell of the inquiry terminal 1 is made of explosion-proof lamp component.
Further, the display module 3 is specially anti-explosion LED display screen.
Further, the alarm module 4 is specially LED flashing lamps.
Further, the output end of the external memory 11 and the input of data update module 10 are electrically connected with.
Further, the microprocessor is provided with distributed power control module, the distributed power control module base
Above-mentioned Hamilton Jacobi Bellman equation and Fu Ke-Planck-Ke Ermoge love equations are solved in finite-difference algorithm,
So as to show that interference perceives power control strategy；
The Hamilton Jacobi Bellman equation is：
Wherein
The Fu Ke-Planck-Ke Ermoge love equations are：
Further, the distributed power control method of the distributed power control module comprises the following steps：
Step one, initialize, and time interval, the state space of dominant interferer and the state space typically disturbed are carried out
Discretization；The time interval, the state space of dominant interferer and the state space typically disturbed will be discretized as X × Y × Z
Space, the time, energy, the iteration step length of interference space is：
Step 2, judges whether to meet iterated conditional, if carrying out next step, otherwise stops iteration；The iterated conditional
Refer to that t, o, i meet t=1 simultaneously:S, o=1:Y and i=1:Z, carries out next step, otherwise stops iteration when eligible；Its
Middle s, Y and Z value are provided in initialization；
Step 3, upgrading interference mean field, and judge whether power level is zero, if it is further upgrading interference is flat
Equal field, if otherwise interference mean field is zero；The upgrading interference mean fieldWhen use formula：
WhereinIt is the mean field value at any point (t, o, i) place in discrete grid block,WithIt is dominant interferer and one respectively
As the variance disturbed, soAnd siIt is the i of dominant interferer o and general user state kinetics, δ respectivelyoAnd δiIt is iteration step length,
Wherein
Step 4, updates Lagrangian and power level, and according to iterated conditional, repeat step two arrives step 4；More
New LagrangianWhen use formula：
WhereinWithIt is the mean field value and cost function value at any point (t, o, i) place in discrete grid block respectively,
WithIt is dominant interferer and the variance typically disturbed, s respectivelyoAnd siBe respectively dominant interferer o and general user i state move
Mechanics, δo、δiAnd δtIt is iteration step length, wherein
Dominant interferer o power levelRenewal use following manner：
WhereinWithIt is the mean field value and cost function value at any point (t, o, i) place in discrete grid block, s respectivelyoWith
siIt is the i of dominant interferer o and general user state kinetics respectively,It is the transmission power of dominant interferer, δoAnd δiIt is iteration
Step-length, wherein
General user i power levelRenewal use following manner：
Wherein each alphabetical implication is referred to as above,It is the transmission power of general user's dominant interferer.
Include The embodiment provides the bore closed quality classification based on cloud computing and method for evaluating hazard
Following steps：
Step 1: according to drilling basic information database, bore closed materials volume and quality database and drilling coal
Layer information database three part, determine in drilling target reservoir in horizontal segment cytoplasmic mutation estimation range；
Step 2: according to all visual angles and the angle of pitch in the length of side of rectangle image device and current region to be detected, adjustment
Full shot on focal plane institute into circular panorama picture so that the full shot on focal plane into the straight of circular panorama picture
Footpath is more than the bond length of rectangle image device；
Step 3: ground model and well drilling rail model of the generation with constraints, set up general target plane equation
And the coordinate transformation relation between target coordinate system and mouth coordinate system, with the target plane suitable for various well type；
Step 4: drilling essential information data are uploaded into cloud computing center in real time；
Step 5: the population scale of initialization drilling essential information data, and population maximum iteration is set；
Step 6: chromosome is generated at random according to coding rule, by the abstract coding input for chromosome of cloud task, dyeing
Each gene in body is the subtask in the cloud task, and the position sequence number of chromosome represents that each subtask is represented and is assigned to virtually
The number of machine；Then judge whether chromosome meets expected Qos, if meeting, the chromosome be added in initial population,
If it is not satisfied, the chromosome is then abandoned, the scale until reaching initial population；
Step 7: judging the user's storage service request received, mistake is returned if service request is not met
False information；It is then storage service request distribution resource by global resource scheduler to meet service request；
Step 8: the deviational survey data based on last two measuring point of drilling trajectory, calculate the track characteristic parameter that section is surveyed at end, it is described
Deviational survey data are well depth, hole angle, azimuth, and the track characteristic parameter is used to characterize the last trajectory shape for surveying section；
Step 9: panorama original image is carried out into chromatic compensation to be compensated rear image, image after this is compensated with
Track characteristic parameter is spliced, and for one group of image of processing defined in multiple stitching images, is made in this group of image at least
One composition alignment, is converted by alignment by the way that one or more image is cut out, is sized and rotated
A series of one or more images to produce process conversion in image；
Step 10: bore closed quality classification evaluation method is from borehole image, the consumption of drilling and sealing material, bore closed
Section carries out classification quantitative assessment to the seal quality of drilling respectively away from three aspects, and at least two parties face evaluation result is qualified, judges
It is qualified for the seal quality of the drilling, otherwise, then determine that it is close bad drilling；
Step 11: close bad drills, water guide hazard assessment is by each dangerous influence factor of initiation drilling water guide
Intrusion Index is calculated and BP artificial neural network modules predict that two parts are constituted.
Further, drilling basic information database is again comprising drilling essential information, and bore closed information, the 4th are and plaster
Rock water-bearing layer information；Wherein essential information include drilling sequence number, title, latitude away from, through away from, aperture absolute altitude, drilling depth, whole aperture layer
Position；Bore closed information is including closing hole structure, bore closed start-stop depth, sealing material and consumption, sealing of hole overview and takes
Sample quality；4th be and marl water-bearing layer information include Quaternary aquifer floor level, marl floor level and thickness；
Bore closed materials volume and quality database contain hole structure information and closed section away from information, hole structure
Information includes aperture, initial depth and terminates depth, closed section away from information include bore closed section away from initial depth, terminate it is deep
Degree, cement consumption and sand consumption；
Drilling coal seam information database contains the floor level that each coal seam cut is worn in drilling, coal seam thickness, Yi Jifeng
Hole section away from starting and terminate height, aperture absolute altitude and peak level.
Further, the dangerous influence factor of water guide includes water filling source, geological structure, mining work activities, and water filling source is wrapped again
Containing Quaternary aquifer, the three sub- factors in mud stone water-bearing layer and old empty water, geological structure includes tomography, fold, mining work activities
Including water flowing fractured zone development height and plate destructing depth, pass through the dangerous shadow of water guide of each influence factor of system-computed
Snap number；BP neural network predictions module includes parameters setting, sample training and sample predictions.
Further, panorama original image is spliced by original closure borehole image, image of the generation by splicing
Data matrix, and centralization or standardization are carried out to data matrix, calculate the data matrix after centralization or standardization
Variance matrix, high order proper polynomial is converted to by the proper polynomial of variance matrix, and the data matrix is entered according to following formula
Row standardization：
In formula：
Obtain：
A=(Aij)m×n；
M is the line number of data matrix, and n is the columns of data matrix, i=1,2 ..., m, j=1,2 ..., n；Xij is data
The data that the i-th row jth is arranged in matrix.
Operation principle：The bore closed quality classification and hazard assessment system based on cloud computing, staff pass through
The related data of bore closed is input in the microprocessor 2 in inquiry terminal 1 by input module 5, and microprocessor 2 passes through ether
Net 7 and data switching exchane 8 send data in cloud computing server 9, and cloud computing server 9 utilizes analysis of data collected module 16
With data processing module 17 data are subjected to double analysis processing, RAM memory 18, mram memory 19 and database 20 are matched somebody with somebody
Conjunction is used, and the related data of drilling can be contrasted, sampled and inquired about, respectively will be qualified and unqualified by sort module 13
Bore closed quality condition store into the first flash card 23 and the second flash card 24, information can be entered using external memory 11
Row export, while the geological condition and parameter that upgrade in time related to data update module 10 using external memory 11, cloud computing
Server 9, by all data Cun Chudao high in the clouds memories 22, further ensures the safety of data by GPRS network 21
Storage, carries out hazard assessment, and pass through emergency plan system by the seal quality situation of 14 pairs of drilling of hazard assessment module
Cover half block 12 causes rapidly the reason for bore closed is off quality and formulates corresponding emergency measure, and drilling is sealed
The quality condition and emergency measure closed is sent in microprocessor 2 by Ethernet 7 and data switching exchane 8, and microprocessor 2 is by phase
Close information to show by display module 3, and alerting is sent by alarm module 4 and remind staff, power supply module 6
Power supply is provided for inquiry terminal 1, cloud computing server 9 can inquire about conventional bore closed information using enquiry module 15.
Bore closed quality classification evaluation method is from borehole image, the consumption of drilling and sealing material, bore closed section away from three
Individual aspect carries out classification quantitative assessment to the seal quality of drilling respectively, and at least two parties face evaluation result is qualified, is determined as the brill
The seal quality in hole is qualified, otherwise, then determines that it is close bad drilling；Close bad drills water guide hazard assessment by each
Trigger the Intrusion Index of the dangerous influence factor of drilling water guide to calculate and predict that two parts are constituted with BP artificial neural network modules.Entirely
Each drilling is assessed to the influence degree of seam mining in face, draws bore closed quality classification evaluation result, it is ensured that safety is raw
Production, the investigation and improvement that drilling can be closed for colliery provides referential experience.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.
Claims (3)
1. a kind of bore closed quality classification and hazard assessment system based on cloud computing, it is characterised in that including inquiry eventually
End and cloud computing server, the inquiry terminal is by microprocessor, display module, alarm module, input module and power supply module
Constitute；
The output end of the microprocessor is electrically connected with the input of display module and alarm module respectively；The microprocessor
Input be electrically connected with respectively with the output end of input module and power supply module；
The microprocessor is connected by Ethernet with data switching exchane；
The cloud computing server is connected by Ethernet with data switching exchane；
The input of the cloud computing server and the output end of data update module are electrically connected with；
The output end of the cloud computing server is formulated module, sort module, danger and commented with external memory, emergency plan respectively
The input of valency module and enquiry module is electrically connected with；
The output end of the cloud computing server is electrically connected by the input of analysis of data collected module and data processing module
Connect；
The cloud computing server is electrically connected with RAM memory, mram memory and database respectively；
The output end of the cloud computing server is connected by GPRS network with the input of high in the clouds memory；
The output end of the sort module is electrically connected with the input of the first flash card and the second flash card respectively；
The shell of the inquiry terminal is made of explosion-proof lamp component, and the display module is specially anti-explosion LED display screen, institute
It is specially LED flashing lamps to state alarm module, and the output end of the external memory and the input of data update module are electrically connected with；
The bore closed matter based on cloud computing of the bore closed quality classification and hazard assessment system based on cloud computing
Amount classification and method for evaluating hazard include：
Step 1: according to drilling basic information database, bore closed materials volume and quality database and drilling coal seam letter
Cease database three part, determine in drilling target reservoir in horizontal segment cytoplasmic mutation estimation range；
Step 2: according to all visual angles and the angle of pitch in the length of side of rectangle image device and current region to be detected, adjusting panorama
Camera lens on focal plane institute into circular panorama picture so that the full shot on focal plane into circular panorama picture diameter it is big
In the bond length of rectangle image device；
Step 3: ground model and well drilling rail model of the generation with constraints, set up general target plane equation and
Coordinate transformation relation between target coordinate system and mouth coordinate system, with the target plane suitable for various well type；
Step 4: drilling essential information data are uploaded into cloud computing center in real time；
Step 5: the population scale of initialization drilling essential information data, and population maximum iteration is set；
Step 6: generate chromosome at random according to coding rule, by the abstract coding input for chromosome of cloud task, in chromosome
Each gene be subtask in the cloud task, the position sequence number of chromosome represents that each subtask is represented and is assigned to virtual machine
Number；Then judge whether chromosome meets expected Qos, if meeting, the chromosome is added in initial population, if not
Meet, then abandon the chromosome, the scale until reaching initial population；
Step 7: judging the user's storage service request received, wrong letter is returned if service request is not met
Breath；It is then storage service request distribution resource by global resource scheduler to meet service request；
Step 8: the deviational survey data based on last two measuring point of drilling trajectory, calculate the track characteristic parameter that section is surveyed at end, the deviational survey
Data are well depth, hole angle, azimuth, and the track characteristic parameter is used to characterize the last trajectory shape for surveying section；
Step 9: panorama original image is subjected to chromatic compensation to be compensated rear image, image and track after this is compensated
Characteristic parameter is spliced, and for one group of image of processing defined in multiple stitching images, makes at least one in this group of image
Composition is alignd, and the image by alignment is converted by the way that one or more image is cut out, is sized and rotated
In it is one or more with produce it is a series of by conversion images；
Step 10: bore closed quality classification evaluation method from borehole image, the consumption of drilling and sealing material, bore closed section away from
Three aspects carry out classification quantitative assessment to the seal quality of drilling respectively, and at least two parties face evaluation result is qualified, is determined as this
The seal quality of drilling is qualified, otherwise, then determines that it is close bad drilling；
Step 11: close bad drills, water guide hazard assessment is triggered the influence of the dangerous influence factor of drilling water guide by each
Index is calculated and BP artificial neural network modules predict that two parts are constituted；
The basic information database that drills is again comprising drilling essential information, and bore closed information, the 4th are and marl water-bearing layer letter
Breath；Wherein essential information include drilling sequence number, title, latitude away from, through away from, aperture absolute altitude, drilling depth, whole aperture layer position；Drilling envelope
Closing information includes closing hole structure, bore closed start-stop depth, sealing material and consumption, sealing of hole overview and sampling quality；The
Four be and marl water-bearing layer information include Quaternary aquifer floor level, marl floor level and thickness；
Bore closed materials volume and quality database contain hole structure information and closed section away from information, hole structure information
Including aperture, initial depth and terminate depth, closed section away from information include bore closed section away from initial depth, terminate depth,
Cement consumption and sand consumption；
Drilling coal seam information database contains the floor level that each coal seam cut is worn in drilling, coal seam thickness, and sealing of hole sections
Away from starting and terminate height, aperture absolute altitude and peak level；
Water guide danger influence factor includes water filling source, geological structure, mining work activities, and water filling source is again aqueous comprising the 4th
Layer, the three sub- factors in mud stone water-bearing layer and old empty water, geological structure include tomography, fold, and mining work activities include water producing fractures
Band development height and plate destructing depth, pass through the dangerous Intrusion Index of water guide of each influence factor of system-computed；BP people
Artificial neural networks prediction module includes parameters setting, sample training and sample predictions；
Panorama original image is spliced by original closure borehole image, the data matrix of image of the generation by splicing, and
Centralization or standardization are carried out to data matrix, the variance matrix of the data matrix after centralization or standardization is calculated, will
The proper polynomial of variance matrix is converted to high order proper polynomial, and the data matrix is standardized according to following formula：
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<msub>
<mi>A</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
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<mi>X</mi>
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</mover>
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</msub>
</mrow>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
</mfrac>
<mo>;</mo>
</mrow>
In formula：
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<msub>
<mover>
<mi>X</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
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<mfrac>
<mn>1</mn>
<mi>m</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
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<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>X</mi>
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<mi>j</mi>
</mrow>
</msub>
<mo>;</mo>
</mrow>
<mrow>
<msub>
<mi>S</mi>
<mi>j</mi>
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<msqrt>
<mrow>
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<mn>1</mn>
<mrow>
<mi>m</mi>
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<mn>1</mn>
</mrow>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mi>=</mi>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mover>
<mi>X</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
<mo>;</mo>
</mrow>
Obtain：
A=(Aij)m×n；
M is the line number of data matrix, and n is the columns of data matrix, i=1,2 ..., m, j=1,2 ..., n；Xij is data matrix
In the i-th row jth arrange data.
2. bore closed quality classification and hazard assessment system as claimed in claim 1 based on cloud computing, it is characterised in that
The microprocessor is provided with distributed power control module, and the distributed power control module is asked based on finite-difference algorithm
Above-mentioned Hamilton Jacobi Bellman equation and Fu Ke-Planck-Ke Ermoge love equations are solved, so as to show that interference is perceived
Power control strategy；
The Hamilton Jacobi Bellman equation is：
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<mi>t</mi>
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<mi>u</mi>
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<mi>H</mi>
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<msub>
<mo>&dtri;</mo>
<mi>s</mi>
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<mi>s</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
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<mo>;</mo>
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Wherein
The Fu Ke-Planck-Ke Ermoge love equations are：
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<mi>t</mi>
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<mi>m</mi>
<mrow>
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<mfrac>
<msubsup>
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<msub>
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<mfrac>
<msubsup>
<mi>&sigma;</mi>
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<mfrac>
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<mi>m</mi>
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<mfrac>
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<msub>
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<msub>
<mi>s</mi>
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<mi>m</mi>
<mrow>
<mo>(</mo>
<mrow>
<mi>t</mi>
<mo>,</mo>
<mi>s</mi>
</mrow>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mn>0.</mn>
</mrow>
3. bore closed quality classification and hazard assessment system as claimed in claim 2 based on cloud computing, it is characterised in that
The distributed power control method of the distributed power control module comprises the following steps：
Step one, initialize, and by time interval, the state space of dominant interferer and the state space progress typically disturbed are discrete
Change；The time interval, the state space of dominant interferer and the state space typically disturbed will be discretized as X × Y × Z sky
Between, the time, energy, the iteration step length of interference space is：
<mrow>
<msub>
<mi>&delta;</mi>
<mi>t</mi>
</msub>
<mo>=</mo>
<mfrac>
<mi>T</mi>
<mi>X</mi>
</mfrac>
<mo>,</mo>
<msub>
<mi>&delta;</mi>
<mi>o</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>I</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mi>Y</mi>
</mfrac>
<mo>,</mo>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>I</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mi>Z</mi>
</mfrac>
<mo>;</mo>
</mrow>
Step 2, judges whether to meet iterated conditional, if carrying out next step, otherwise stops iteration；The iterated conditional refers to
It is t, o, i meets t=1 simultaneously:S, o=1:Y and i=1:Z, carries out next step, otherwise stops iteration when eligible；Wherein s,
Y and Z value is provided in initialization；
Step 3, upgrading interference mean field, and judge whether power level is zero, if it is further upgrading interference is average
, if otherwise interference mean field is zero；The upgrading interference mean fieldWhen use formula：
<mrow>
<mfrac>
<msubsup>
<mi>m</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>&delta;</mi>
<mi>t</mi>
</msub>
</mfrac>
<mo>=</mo>
<msubsup>
<mi>m</mi>
<mrow>
<mi>o</mi>
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<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mfrac>
<mn>1</mn>
<msub>
<mi>&delta;</mi>
<mi>t</mi>
</msub>
</mfrac>
<mo>-</mo>
<mfrac>
<msubsup>
<mi>&sigma;</mi>
<mi>o</mi>
<mn>2</mn>
</msubsup>
<mrow>
<mn>2</mn>
<msub>
<mi>&delta;</mi>
<mi>o</mi>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mfrac>
<msubsup>
<mi>&sigma;</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mrow>
<mn>2</mn>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
</msub>
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</mfrac>
<mo>-</mo>
<mn>2</mn>
<mfrac>
<msub>
<mi>s</mi>
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</msub>
<msubsup>
<mi>&delta;</mi>
<mi>o</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>-</mo>
<mn>2</mn>
<mfrac>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<msubsup>
<mi>&delta;</mi>
<mi>i</mi>
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</mfrac>
<mo>)</mo>
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<mrow>
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<msubsup>
<mi>&sigma;</mi>
<mi>o</mi>
<mn>2</mn>
</msubsup>
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<msub>
<mi>&delta;</mi>
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<mo>+</mo>
<mfrac>
<msub>
<mi>s</mi>
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<mo>)</mo>
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<msubsup>
<mi>m</mi>
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<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
<mrow>
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<mfrac>
<msubsup>
<mi>&sigma;</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
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<msub>
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<mfrac>
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<mi>m</mi>
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</mrow>
<mi>t</mi>
</msubsup>
<mfrac>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<msubsup>
<mi>&delta;</mi>
<mi>i</mi>
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</mfrac>
</mrow>
WhereinIt is the mean field value at any point (t, o, i) place in discrete grid block,WithIt is dominant interferer respectively and general dry
The variance disturbed, so and siIt is the i of dominant interferer o and general user state kinetics, δ o and δ respectivelyiIt is iteration step length, wherein
Step 4, updates Lagrangian and power level, and according to iterated conditional, repeat step two arrives step 4；Update and draw
Ge Lang operatorsWhen use formula：
<mrow>
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</munderover>
<msubsup>
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<mi>t</mi>
</msubsup>
</mrow>
<msubsup>
<mi>&delta;</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>;</mo>
</mrow>
WhereinWithIt is the mean field value and cost function value at any point (t, o, i) place in discrete grid block respectively,With
It is dominant interferer and the variance typically disturbed respectively, so and siIt is the i of dominant interferer o and general user state power respectively
Learn, δ o, δiAnd δtIt is iteration step length, wherein
Dominant interferer o power levelRenewal use following manner：
<mrow>
<msubsup>
<mi>m</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mfrac>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>c</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>p</mi>
<mi>o</mi>
<mi>t</mi>
</msubsup>
</mrow>
</mfrac>
<mo>-</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>s</mi>
<mi>o</mi>
</msub>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>p</mi>
<mi>o</mi>
<mi>t</mi>
</msubsup>
</mrow>
</mfrac>
<mfrac>
<mrow>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>-</mo>
<mn>2</mn>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>-</mo>
<mn>1</mn>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
<msubsup>
<mi>&delta;</mi>
<mi>o</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>-</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>p</mi>
<mi>o</mi>
<mi>t</mi>
</msubsup>
</mrow>
</mfrac>
<mfrac>
<mrow>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
<mo>-</mo>
<mn>2</mn>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
<msubsup>
<mi>&delta;</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>=</mo>
<mn>0</mn>
</mrow>
WhereinWithIt is the mean field value and cost function value, so and s at any point (t, o, i) place in discrete grid block respectivelyiPoint
It is not the i of dominant interferer o and general user state kinetics,It is the transmission power of dominant interferer, δ o and δiIt is iteration step
It is long, wherein
General user i power levelRenewal use following manner：
<mrow>
<msubsup>
<mi>m</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mfrac>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>c</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>p</mi>
<mi>i</mi>
<mi>t</mi>
</msubsup>
</mrow>
</mfrac>
<mo>-</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>s</mi>
<mi>o</mi>
</msub>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>p</mi>
<mi>i</mi>
<mi>t</mi>
</msubsup>
</mrow>
</mfrac>
<mfrac>
<mrow>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>-</mo>
<mn>2</mn>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>-</mo>
<mn>1</mn>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
<msubsup>
<mi>&delta;</mi>
<mi>o</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>-</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>p</mi>
<mi>i</mi>
<mi>t</mi>
</msubsup>
</mrow>
</mfrac>
<mfrac>
<mrow>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
<mo>-</mo>
<mn>2</mn>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&lambda;</mi>
<mrow>
<mi>o</mi>
<mo>,</mo>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
<msubsup>
<mi>&delta;</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>=</mo>
<mn>0</mn>
<mo>;</mo>
</mrow>
Wherein each alphabetical implication is referred to as above,It is the transmission power of general user's dominant interferer.
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Application publication date: 20170825 |